Technical Field
The present disclosure relates to the field of cephalometric analysis in orthodontics. More particularly, the disclosure relates to the method of automated cephalometric landmark detection on volumetric data, used by orthodontic specialists in cephalometric analysis for diagnosis and treatment planning of their patients.
Description of the Related Art
In dentistry, Orthodontic specialists use cephalometric analysis for diagnosis and treatment planning of patient's dento-maxillofacial and craniofacial deformity. In case of surgery, growth prediction or evaluation, monitoring treatment outcome cephalometric analysis is needed. It is based on geometrical measurements such as distances and angle. These measurements are recorded among standardly defined anatomical points called as landmarks. Calculated measurements of a patient are compared with standardly existing normal values according to the patient's race and ethnicity.
In the past, measurements, calculation and analysis were conducted manually by placing tracing sheets on the X-Ray film, which was error prone. Currently, computerized analysis is in clinical practice. Analysis is performed using plotted cephalometric landmarks on scanned/digital 2-D X-Ray films or 3-D CT/CBCT scan volumetric data of the skull. Plotting of landmarks takes time and efforts of an orthodontic specialist thus being tedious and time consuming. Also repeatability and reproducibility may be affected. Hence, computerized 2-D cephalometric analysis software is available for helping orthodontist. Analysis on three-dimensional frame is used to avoid the problems in two-dimensional radiographs. But, manual marking and plotting of landmarks on 3-D data is more difficult and exhausting with the appearance of third dimension. Thus, a method is proposed for searching landmarks automatically on 3-D volumetric data for assisting orthodontic surgeons.
The prior art uses marginal space learning geometrical model for localization of 3-D landmarks. It requires a training set for correct position and orientation. Hence, accuracy is not promising due to localization of points on the basis of distance learning based training. Another approach aligns patient image and training image of already localized points, using positional scaling and rotation. Then, correct position of a point is searched using similarity search for a feature. As the patient geometry is variable, results from similarity search are not promising. Another approach uses image adaptive transformation with already traced cephalometric image for anatomical landmark detection. This approach is similar as registration of two images.
At least some of the disclosed embodiments do not use any training set or registration procedure. In some embodiments, clustering of certain landmarks in a group and corresponding region is identified using Empirical Point calculated from a reference point. Corresponding Mathematical. Entity on detected contour gives the location of cephalometric landmark.
In contrast, prior art suffers from at least the following drawbacks.    U.S. Pat. No. 8,023,706 B2 Sep. 20, 2011 Automatically Determining Landmarks On Anatomical Structure
This patent discloses converting sample image into patient image, by transforming anatomical structure of sample data into anatomical structure of patient data using morphing and image fusion algorithms. Similarly, cephalometric landmarks are identified on patient image by transformation of a sample image.
The transformation of sample image to a patient image is the drawback of this patent. By transformation of a standard image cannot promise for accurate results on the patient image.    U.S. Pat. No. 8,363,918 B2 Jan. 29, 2013 Method and System For Anatomic Landmark Detection Using Constrained Marginal Space Learning And Geometric Interface
This patent discloses detecting first landmark using Marginal Space Learning (MSL) and remaining landmarks based on geometrical model. Geometrical model is trained with manual cephalometric landmarks on various datasets.
The drawback of this system is that it has to calculate object position, position orientation and similarity transformation factors for transforming learned geometrical model to patient three-dimensional model. The estimation of these factors from trained model does not promise for accurate measure of cephalometric landmark position,    U.S. Pat. No. 8,160,322 B2 Apr. 17, 2012 Joint Detection And Localization Of Multiple Anatomical Landmarks Through Learning
This patent discloses detecting anatomical landmarks in medical images and verifies its locations through spatial statistics.
The system is made for detecting anatomical landmarks using training database classifiers. The approach of the patent is generic for whole body anatomical landmarks. It comments neither for 2D or 3D landmarks nor for cephalometric landmarks in specific.    U.S. Pat. No. 8,218,849 B2 Jul. 10, 2012, Method And System For Automatic Landmark Detection Using Discriminative Joint Context
This patent discloses detecting anatomical landmarks in heart Magnetic Resonance Imaging (MRI) using joint context. This method cannot be applied for cephalometric landmark detection.    U.S. Pat. No. 8,160,677 B2 Apr. 17, 2012 Method For Identification of Anatomical Landmarks
This patent discloses detecting anatomical landmarks in brain Magnetic Resonance Imaging (MRI) and a combination of steps cannot be applied for cephalometric landmark detection.    U.S. Pat. No. 8,150,498 B2 Apr. 3, 2012 System For Identification Of Anatomical Landmarks
This patent discloses detecting anatomical landmarks in brain Magnetic Resonance Imaging (MRI) and a combination of steps cannot be applied for cephalometric landmark detection.    U.S. Pat. No. 7,783,090 B2 Aug. 24, 2010 Automatic Identification Of The Anterior And Posterior Commissure Landmarks
This patent discloses detecting anterior and posterior commissure landmarks in brain Magnetic Resonance Imaging (MRI) and a combination of steps cannot be applied for cephalometric landmark detection.    S. Shahidi, E. Bahrampour, E. Soltanimehr, A. Zamani, M. Oshagh, M. Moattari, et al., “The accuracy of a designed software for automated localization of craniofacial landmarks on CBCT images,” BMC Med Imaging, vol. 14, pp. 1471-2342, 2014
This work proposed a method of automatic landmark detection based on the registration of test image over training image dataset. Registration is based on translation, rotation and scaling of training image and test image in all three axes. Translation is based on the center of gravity and principal axes of the 3D image,
The anatomy of each patient has a unique geometrical structure; therefore it cannot be overlapped properly over the anatomy of training data. Hence, the translation of landmarks from training to test image is error prone and promising results cannot be obtained.    S. Canter et al. “3D cephalometry: a new approach for landmark identification and image orientation” IFMBE Proceedings 2008
This work explained a method of 3-D semi-automatic cephalometric landmark detection. A small region of points is detected manually which is a group of points where every point may be the landmark with a greater error. To find the most accurate point and identify it as a landmark from the group of points is performed automatically. It is difficult to identify the landmark accurately from the group of points manually. Hence, for increasing accuracy, one point is selected from the group of points by use of the definition of that particular landmark.
In the disclosed embodiments, these drawbacks have been removed and is based on the knowledge derived from the human anatomy. The anatomical definitions are transformed into mathematical entities for the detection of the landmarks which may be different or common for most of the landmarks. The knowledge is derived for each new landmark on the basis of its anatomical structure.